basicspace (version 0.25)

predict.blackbox: Predict method of blackbox objects

Description

predict.blackbox reads an blackbox object and uses the estimates to generate a matrix of predicted values.

Usage

# S3 method for blackbox
predict(object, dims=1, ...)

Value

A matrix of predicted values generated from the parameters estimated from a blackbox object.

Arguments

object

A blackbox output object.

dims

Number of dimensions used in prediction. Must be equal to or less than number of dimensions used in estimation.

...

Ignored.

Author

Keith Poole

Howard Rosenthal

Jeffrey Lewis

James Lo

Royce Carroll

Christopher Hare

References

David A. Armstrong II, Ryan Bakker, Royce Carroll, Christopher Hare, Keith T. Poole, and Howard Rosenthal. 2021. Analyzing Spatial Models of Choice and Judgment. 2nd ed. Statistics in the Social and Behavioral Sciences Series. Boca Raton, FL: Chapman & Hall/CRC. doi: 10.1201/9781315197609

Keith T. Poole, Jeffrey B. Lewis, Howard Rosenthal, James Lo, and Royce Carroll. 2016. ``Recovering a Basic Space from Issue Scales in R.'' Journal of Statistical Software 69(7): 1-21. doi:10.18637/jss.v069.i07

Keith T. Poole. 1998. ``Recovering a Basic Space From a Set of Issue Scales.'' American Journal of Political Science 42(3): 954-993. doi: 10.2307/2991737

See Also

'blackbox', 'Issues1980'

Examples

  ### Loads issue scales from the 1980 ANES.
  data(Issues1980)
  Issues1980[Issues1980[,"abortion1"]==7,"abortion1"] <- 8	#missing recode
  Issues1980[Issues1980[,"abortion2"]==7,"abortion2"] <- 8	#missing recode

  ### Estimate blackbox object from example and call predict function
  # \donttest{ 
  Issues1980_bb <- blackbox(Issues1980, missing=c(0,8,9), verbose=FALSE, 
    dims=3, minscale=8)
  # }
  ### 'Issues1980_bb' can be retrieved quickly with: 
  data(Issues1980_bb)
  prediction <- predict.blackbox(Issues1980_bb, dims=3)

  ### Examine predicted vs. observed values for first 10 respondents
  ### Note that 4th and 6th respondents are NA because of missing data
  Issues1980[1:10,]
  prediction[1:10,]

  ### Check correlation across all predicted vs. observed, excluding missing values
  prediction[which(Issues1980 %in% c(0,8,9))] <- NA
  cor(as.numeric(prediction), as.numeric(Issues1980), use="pairwise.complete")